ML Projects
End-to-end machine learning projects demonstrating practical skills, from problem framing to production deployment.
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Customer Churn Prediction
tabularchurnproduction
Predicting which customers are likely to churn for a SaaS platform.
Approach:Baseline: Logistic Regression → XGBoost → Feature selection → SHAP analysis.
Key Metric:AUC: 0.89, Precision@Top10%: 0.72
NLP Support Ticket Routing
nlpclassificationbert
Automated routing of support tickets using NLP classification.
Approach:Baseline: TF-IDF + Logistic Regression → fine-tuned BERT → error analysis.
Key Metric:Macro F1: 0.81, Routing accuracy: 92%
Energy Demand Forecasting
time-seriesforecastinglstm
Forecasting hourly energy demand for a utility provider.
Approach:Baseline: ARIMA → LSTM → Feature engineering → Hyperparameter tuning.
Key Metric:MAE: 0.13, RMSE: 0.21
RAG-powered Knowledge Base
ragllmretrieval
Retrieval-Augmented Generation for internal knowledge base Q&A.
Approach:Baseline: BM25 → Dense retrieval → OpenAI GPT-3.5 → RAG pipeline.
Key Metric:Top-1 accuracy: 84%, Mean reciprocal rank: 0.78